Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations999
Missing cells452
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory848.2 KiB
Average record size in memory869.4 B

Variable types

Numeric12
Text8
Categorical1

Alerts

Rating is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Revenue is highly overall correlated with Votes and 4 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with Rating and 1 other fieldsHigh correlation
Unnamed: 0.1 is highly overall correlated with Rating and 1 other fieldsHigh correlation
Votes is highly overall correlated with Revenue and 4 other fieldsHigh correlation
tmdb_budget is highly overall correlated with Revenue and 4 other fieldsHigh correlation
tmdb_popularity is highly overall correlated with Revenue and 4 other fieldsHigh correlation
tmdb_revenue is highly overall correlated with Revenue and 4 other fieldsHigh correlation
tmdb_vote_count is highly overall correlated with Revenue and 4 other fieldsHigh correlation
Certificate has 101 (10.1%) missing values Missing
scoreAvg has 157 (15.7%) missing values Missing
Revenue has 169 (16.9%) missing values Missing
Unnamed: 0.1 is uniformly distributed Uniform
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0.1 has unique values Unique
Unnamed: 0 has unique values Unique
Overview has unique values Unique
tmdb_budget has 150 (15.0%) zeros Zeros
tmdb_revenue has 112 (11.2%) zeros Zeros

Reproduction

Analysis started2025-09-02 13:55:35.368843
Analysis finished2025-09-02 13:56:58.479785
Duration1 minute and 23.11 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Unnamed: 0.1
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499
Minimum0
Maximum998
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:56:58.858721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.9
Q1249.5
median499
Q3748.5
95-th percentile948.1
Maximum998
Range998
Interquartile range (IQR)499

Descriptive statistics

Standard deviation288.53076
Coefficient of variation (CV)0.57821796
Kurtosis-1.2
Mean499
Median Absolute Deviation (MAD)250
Skewness0
Sum498501
Variance83250
MonotonicityStrictly increasing
2025-09-02T10:56:59.443510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
998 1
 
0.1%
0 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
982 1
 
0.1%
981 1
 
0.1%
980 1
 
0.1%
Other values (989) 989
99.0%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%
991 1
0.1%
990 1
0.1%
989 1
0.1%

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:56:59.925556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.9
Q1250.5
median500
Q3749.5
95-th percentile949.1
Maximum999
Range998
Interquartile range (IQR)499

Descriptive statistics

Standard deviation288.53076
Coefficient of variation (CV)0.57706152
Kurtosis-1.2
Mean500
Median Absolute Deviation (MAD)250
Skewness0
Sum499500
Variance83250
MonotonicityStrictly increasing
2025-09-02T10:57:00.440623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
983 1
 
0.1%
982 1
 
0.1%
981 1
 
0.1%
Other values (989) 989
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%
991 1
0.1%
990 1
0.1%

Title
Text

Distinct998
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
2025-09-02T10:57:01.688201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length68
Median length41
Mean length15.443443
Min length2

Characters and Unicode

Total characters15428
Distinct characters100
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique997 ?
Unique (%)99.8%

Sample

1st rowThe Godfather
2nd rowThe Dark Knight
3rd rowThe Godfather: Part II
4th row12 Angry Men
5th rowThe Lord of the Rings: The Return of the King
ValueCountFrequency (%)
the 274
 
9.8%
of 86
 
3.1%
a 32
 
1.2%
and 28
 
1.0%
no 24
 
0.9%
la 23
 
0.8%
in 22
 
0.8%
to 18
 
0.6%
de 17
 
0.6%
man 17
 
0.6%
Other values (1664) 2241
80.6%
2025-09-02T10:57:03.248513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1783
 
11.6%
e 1425
 
9.2%
a 1126
 
7.3%
o 965
 
6.3%
n 921
 
6.0%
i 861
 
5.6%
r 816
 
5.3%
t 755
 
4.9%
h 564
 
3.7%
s 562
 
3.6%
Other values (90) 5650
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11162
72.3%
Uppercase Letter 2191
 
14.2%
Space Separator 1783
 
11.6%
Other Punctuation 177
 
1.1%
Decimal Number 79
 
0.5%
Dash Punctuation 31
 
0.2%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Other Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1425
12.8%
a 1126
10.1%
o 965
 
8.6%
n 921
 
8.3%
i 861
 
7.7%
r 816
 
7.3%
t 755
 
6.8%
h 564
 
5.1%
s 562
 
5.0%
l 514
 
4.6%
Other values (38) 2653
23.8%
Uppercase Letter
ValueCountFrequency (%)
T 283
 
12.9%
S 187
 
8.5%
B 157
 
7.2%
M 139
 
6.3%
D 129
 
5.9%
L 119
 
5.4%
A 113
 
5.2%
C 101
 
4.6%
H 98
 
4.5%
P 97
 
4.4%
Other values (18) 768
35.1%
Decimal Number
ValueCountFrequency (%)
2 23
29.1%
1 15
19.0%
0 11
13.9%
3 8
 
10.1%
4 5
 
6.3%
7 5
 
6.3%
9 4
 
5.1%
5 4
 
5.1%
8 2
 
2.5%
6 2
 
2.5%
Other Punctuation
ValueCountFrequency (%)
: 62
35.0%
. 47
26.6%
' 32
18.1%
, 16
 
9.0%
! 7
 
4.0%
& 6
 
3.4%
? 3
 
1.7%
/ 3
 
1.7%
· 1
 
0.6%
Space Separator
ValueCountFrequency (%)
1783
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13353
86.6%
Common 2075
 
13.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1425
 
10.7%
a 1126
 
8.4%
o 965
 
7.2%
n 921
 
6.9%
i 861
 
6.4%
r 816
 
6.1%
t 755
 
5.7%
h 564
 
4.2%
s 562
 
4.2%
l 514
 
3.8%
Other values (66) 4844
36.3%
Common
ValueCountFrequency (%)
1783
85.9%
: 62
 
3.0%
. 47
 
2.3%
' 32
 
1.5%
- 31
 
1.5%
2 23
 
1.1%
, 16
 
0.8%
1 15
 
0.7%
0 11
 
0.5%
3 8
 
0.4%
Other values (14) 47
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15362
99.6%
None 66
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1783
 
11.6%
e 1425
 
9.3%
a 1126
 
7.3%
o 965
 
6.3%
n 921
 
6.0%
i 861
 
5.6%
r 816
 
5.3%
t 755
 
4.9%
h 564
 
3.7%
s 562
 
3.7%
Other values (64) 5584
36.3%
None
ValueCountFrequency (%)
ô 14
21.2%
é 6
 
9.1%
û 5
 
7.6%
è 5
 
7.6%
â 5
 
7.6%
ä 4
 
6.1%
î 2
 
3.0%
ù 2
 
3.0%
ü 2
 
3.0%
á 2
 
3.0%
Other values (16) 19
28.8%

Year
Real number (ℝ)

Distinct99
Distinct (%)9.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1991.2144
Minimum1920
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:57:03.616809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1920
5-th percentile1944
Q11976
median1999
Q32009
95-th percentile2017
Maximum2020
Range100
Interquartile range (IQR)33

Descriptive statistics

Standard deviation23.308539
Coefficient of variation (CV)0.01170569
Kurtosis-0.02478235
Mean1991.2144
Median Absolute Deviation (MAD)14
Skewness-0.93854006
Sum1987232
Variance543.28798
MonotonicityNot monotonic
2025-09-02T10:57:04.132031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 32
 
3.2%
2004 31
 
3.1%
2009 29
 
2.9%
2013 28
 
2.8%
2016 28
 
2.8%
2001 27
 
2.7%
2006 26
 
2.6%
2007 26
 
2.6%
2015 25
 
2.5%
2012 24
 
2.4%
Other values (89) 722
72.3%
ValueCountFrequency (%)
1920 1
 
0.1%
1921 1
 
0.1%
1922 1
 
0.1%
1924 1
 
0.1%
1925 2
0.2%
1926 1
 
0.1%
1927 2
0.2%
1928 2
0.2%
1930 1
 
0.1%
1931 3
0.3%
ValueCountFrequency (%)
2020 6
 
0.6%
2019 23
2.3%
2018 19
1.9%
2017 22
2.2%
2016 28
2.8%
2015 25
2.5%
2014 32
3.2%
2013 28
2.8%
2012 24
2.4%
2011 18
1.8%

Certificate
Categorical

Missing 

Distinct16
Distinct (%)1.8%
Missing101
Missing (%)10.1%
Memory size58.0 KiB
U
234 
A
196 
UA
175 
R
146 
PG-13
43 
Other values (11)
104 

Length

Max length8
Median length1
Mean length1.7371938
Min length1

Characters and Unicode

Total characters1560
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st rowA
2nd rowUA
3rd rowA
4th rowU
5th rowU

Common Values

ValueCountFrequency (%)
U 234
23.4%
A 196
19.6%
UA 175
17.5%
R 146
14.6%
PG-13 43
 
4.3%
PG 37
 
3.7%
Passed 34
 
3.4%
G 12
 
1.2%
Approved 11
 
1.1%
TV-PG 3
 
0.3%
Other values (6) 7
 
0.7%
(Missing) 101
10.1%

Length

2025-09-02T10:57:04.578974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
u 234
26.1%
a 196
21.8%
ua 175
19.5%
r 146
16.3%
pg-13 43
 
4.8%
pg 37
 
4.1%
passed 34
 
3.8%
g 12
 
1.3%
approved 11
 
1.2%
tv-pg 3
 
0.3%
Other values (6) 7
 
0.8%

Most occurring characters

ValueCountFrequency (%)
U 411
26.3%
A 384
24.6%
R 146
 
9.4%
P 119
 
7.6%
G 97
 
6.2%
s 68
 
4.4%
- 48
 
3.1%
e 46
 
2.9%
d 46
 
2.9%
1 45
 
2.9%
Other values (14) 150
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1168
74.9%
Lowercase Letter 253
 
16.2%
Decimal Number 90
 
5.8%
Dash Punctuation 48
 
3.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 68
26.9%
e 46
18.2%
d 46
18.2%
a 35
13.8%
p 22
 
8.7%
r 12
 
4.7%
o 11
 
4.3%
v 11
 
4.3%
n 1
 
0.4%
t 1
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
U 411
35.2%
A 384
32.9%
R 146
 
12.5%
P 119
 
10.2%
G 97
 
8.3%
T 5
 
0.4%
V 5
 
0.4%
M 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 45
50.0%
3 43
47.8%
4 1
 
1.1%
6 1
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1421
91.1%
Common 139
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 411
28.9%
A 384
27.0%
R 146
 
10.3%
P 119
 
8.4%
G 97
 
6.8%
s 68
 
4.8%
e 46
 
3.2%
d 46
 
3.2%
a 35
 
2.5%
p 22
 
1.5%
Other values (8) 47
 
3.3%
Common
ValueCountFrequency (%)
- 48
34.5%
1 45
32.4%
3 43
30.9%
4 1
 
0.7%
6 1
 
0.7%
/ 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 411
26.3%
A 384
24.6%
R 146
 
9.4%
P 119
 
7.6%
G 97
 
6.2%
s 68
 
4.4%
- 48
 
3.1%
e 46
 
2.9%
d 46
 
2.9%
1 45
 
2.9%
Other values (14) 150
 
9.6%

Runtime
Real number (ℝ)

Distinct140
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.87187
Minimum45
Maximum321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:57:04.977879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile87
Q1103
median119
Q3137
95-th percentile178
Maximum321
Range276
Interquartile range (IQR)34

Descriptive statistics

Standard deviation28.101227
Coefficient of variation (CV)0.2287035
Kurtosis3.4289066
Mean122.87187
Median Absolute Deviation (MAD)17
Skewness1.2098771
Sum122749
Variance789.67896
MonotonicityNot monotonic
2025-09-02T10:57:05.542907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 23
 
2.3%
130 23
 
2.3%
129 22
 
2.2%
101 22
 
2.2%
113 22
 
2.2%
110 20
 
2.0%
122 20
 
2.0%
108 19
 
1.9%
102 18
 
1.8%
96 17
 
1.7%
Other values (130) 793
79.4%
ValueCountFrequency (%)
45 1
 
0.1%
64 1
 
0.1%
67 1
 
0.1%
68 1
 
0.1%
69 1
 
0.1%
70 1
 
0.1%
71 2
0.2%
72 2
0.2%
75 2
0.2%
76 3
0.3%
ValueCountFrequency (%)
321 1
0.1%
242 1
0.1%
238 1
0.1%
229 1
0.1%
228 1
0.1%
224 1
0.1%
220 1
0.1%
212 1
0.1%
210 1
0.1%
209 1
0.1%

Genre
Text

Distinct202
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Memory size74.3 KiB
2025-09-02T10:57:06.070920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length24
Mean length19.077077
Min length5

Characters and Unicode

Total characters19058
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)7.2%

Sample

1st rowCrime, Drama
2nd rowAction, Crime, Drama
3rd rowCrime, Drama
4th rowCrime, Drama
5th rowAction, Adventure, Drama
ValueCountFrequency (%)
drama 723
28.5%
comedy 233
 
9.2%
crime 209
 
8.2%
adventure 196
 
7.7%
action 189
 
7.4%
thriller 137
 
5.4%
romance 125
 
4.9%
biography 109
 
4.3%
mystery 99
 
3.9%
animation 82
 
3.2%
Other values (11) 438
17.2%
2025-09-02T10:57:06.910012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2018
 
10.6%
r 1871
 
9.8%
, 1541
 
8.1%
1541
 
8.1%
m 1447
 
7.6%
e 1235
 
6.5%
i 1144
 
6.0%
o 896
 
4.7%
n 760
 
4.0%
t 727
 
3.8%
Other values (23) 5878
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13264
69.6%
Uppercase Letter 2626
 
13.8%
Other Punctuation 1541
 
8.1%
Space Separator 1541
 
8.1%
Dash Punctuation 86
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2018
15.2%
r 1871
14.1%
m 1447
10.9%
e 1235
9.3%
i 1144
8.6%
o 896
6.8%
n 760
 
5.7%
t 727
 
5.5%
y 718
 
5.4%
c 433
 
3.3%
Other values (8) 2015
15.2%
Uppercase Letter
ValueCountFrequency (%)
D 723
27.5%
A 467
17.8%
C 442
16.8%
F 208
 
7.9%
M 151
 
5.8%
T 137
 
5.2%
R 125
 
4.8%
B 109
 
4.2%
H 88
 
3.4%
S 86
 
3.3%
Other values (2) 90
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 1541
100.0%
Space Separator
ValueCountFrequency (%)
1541
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15890
83.4%
Common 3168
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2018
12.7%
r 1871
11.8%
m 1447
 
9.1%
e 1235
 
7.8%
i 1144
 
7.2%
o 896
 
5.6%
n 760
 
4.8%
t 727
 
4.6%
D 723
 
4.6%
y 718
 
4.5%
Other values (20) 4351
27.4%
Common
ValueCountFrequency (%)
, 1541
48.6%
1541
48.6%
- 86
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19058
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2018
 
10.6%
r 1871
 
9.8%
, 1541
 
8.1%
1541
 
8.1%
m 1447
 
7.6%
e 1235
 
6.5%
i 1144
 
6.0%
o 896
 
4.7%
n 760
 
4.0%
t 727
 
3.8%
Other values (23) 5878
30.8%

Rating
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9479479
Minimum7.6
Maximum9.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:57:07.146873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile7.6
Q17.7
median7.9
Q38.1
95-th percentile8.5
Maximum9.2
Range1.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.27228951
Coefficient of variation (CV)0.034259096
Kurtosis1.0583968
Mean7.9479479
Median Absolute Deviation (MAD)0.2
Skewness0.94669269
Sum7940
Variance0.074141576
MonotonicityDecreasing
2025-09-02T10:57:07.495177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.7 157
15.7%
7.8 151
15.1%
8 141
14.1%
8.1 127
12.7%
7.6 123
12.3%
7.9 106
10.6%
8.2 67
6.7%
8.3 44
 
4.4%
8.4 31
 
3.1%
8.5 20
 
2.0%
Other values (6) 32
 
3.2%
ValueCountFrequency (%)
7.6 123
12.3%
7.7 157
15.7%
7.8 151
15.1%
7.9 106
10.6%
8 141
14.1%
8.1 127
12.7%
8.2 67
6.7%
8.3 44
 
4.4%
8.4 31
 
3.1%
8.5 20
 
2.0%
ValueCountFrequency (%)
9.2 1
 
0.1%
9 3
 
0.3%
8.9 3
 
0.3%
8.8 5
 
0.5%
8.7 5
 
0.5%
8.6 15
 
1.5%
8.5 20
 
2.0%
8.4 31
3.1%
8.3 44
4.4%
8.2 67
6.7%

Overview
Text

Unique 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size201.4 KiB
2025-09-02T10:57:08.816820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length313
Median length197
Mean length146.28328
Min length40

Characters and Unicode

Total characters146137
Distinct characters86
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique999 ?
Unique (%)100.0%

Sample

1st rowAn organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.
2nd rowWhen the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.
3rd rowThe early life and career of Vito Corleone in 1920s New York City is portrayed, while his son, Michael, expands and tightens his grip on the family crime syndicate.
4th rowA jury holdout attempts to prevent a miscarriage of justice by forcing his colleagues to reconsider the evidence.
5th rowGandalf and Aragorn lead the World of Men against Sauron's army to draw his gaze from Frodo and Sam as they approach Mount Doom with the One Ring.
ValueCountFrequency (%)
a 1609
 
6.4%
the 1206
 
4.8%
to 803
 
3.2%
of 777
 
3.1%
and 696
 
2.8%
in 565
 
2.3%
his 516
 
2.1%
an 291
 
1.2%
is 245
 
1.0%
with 242
 
1.0%
Other values (5878) 18034
72.2%
2025-09-02T10:57:10.705129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23999
16.4%
e 13867
 
9.5%
a 9800
 
6.7%
t 9329
 
6.4%
i 8842
 
6.1%
n 8580
 
5.9%
o 8559
 
5.9%
r 8202
 
5.6%
s 7965
 
5.5%
h 5625
 
3.8%
Other values (76) 41369
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 114964
78.7%
Space Separator 24000
 
16.4%
Uppercase Letter 3515
 
2.4%
Other Punctuation 2721
 
1.9%
Decimal Number 509
 
0.3%
Dash Punctuation 395
 
0.3%
Open Punctuation 13
 
< 0.1%
Close Punctuation 13
 
< 0.1%
Currency Symbol 4
 
< 0.1%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13867
12.1%
a 9800
 
8.5%
t 9329
 
8.1%
i 8842
 
7.7%
n 8580
 
7.5%
o 8559
 
7.4%
r 8202
 
7.1%
s 7965
 
6.9%
h 5625
 
4.9%
l 4847
 
4.2%
Other values (23) 29348
25.5%
Uppercase Letter
ValueCountFrequency (%)
A 712
20.3%
T 258
 
7.3%
I 258
 
7.3%
W 228
 
6.5%
S 223
 
6.3%
B 176
 
5.0%
M 167
 
4.8%
C 158
 
4.5%
H 139
 
4.0%
R 119
 
3.4%
Other values (17) 1077
30.6%
Decimal Number
ValueCountFrequency (%)
1 117
23.0%
0 104
20.4%
9 94
18.5%
2 43
 
8.4%
6 33
 
6.5%
7 30
 
5.9%
5 26
 
5.1%
8 23
 
4.5%
4 21
 
4.1%
3 18
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 1278
47.0%
, 1082
39.8%
' 260
 
9.6%
" 60
 
2.2%
: 16
 
0.6%
? 11
 
0.4%
/ 8
 
0.3%
; 6
 
0.2%
Space Separator
ValueCountFrequency (%)
23999
> 99.9%
  1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 395
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 4
100.0%
Final Punctuation
ValueCountFrequency (%)
» 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118479
81.1%
Common 27658
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13867
11.7%
a 9800
 
8.3%
t 9329
 
7.9%
i 8842
 
7.5%
n 8580
 
7.2%
o 8559
 
7.2%
r 8202
 
6.9%
s 7965
 
6.7%
h 5625
 
4.7%
l 4847
 
4.1%
Other values (50) 32863
27.7%
Common
ValueCountFrequency (%)
23999
86.8%
. 1278
 
4.6%
, 1082
 
3.9%
- 395
 
1.4%
' 260
 
0.9%
1 117
 
0.4%
0 104
 
0.4%
9 94
 
0.3%
" 60
 
0.2%
2 43
 
0.2%
Other values (16) 226
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146116
> 99.9%
None 21
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23999
16.4%
e 13867
 
9.5%
a 9800
 
6.7%
t 9329
 
6.4%
i 8842
 
6.1%
n 8580
 
5.9%
o 8559
 
5.9%
r 8202
 
5.6%
s 7965
 
5.5%
h 5625
 
3.8%
Other values (65) 41348
28.3%
None
ValueCountFrequency (%)
é 9
42.9%
» 2
 
9.5%
è 2
 
9.5%
ü 1
 
4.8%
  1
 
4.8%
ä 1
 
4.8%
ç 1
 
4.8%
« 1
 
4.8%
ö 1
 
4.8%
É 1
 
4.8%

scoreAvg
Real number (ℝ)

Missing 

Distinct63
Distinct (%)7.5%
Missing157
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean77.969121
Minimum28
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:57:11.102180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile56
Q170
median79
Q387
95-th percentile96
Maximum100
Range72
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.383257
Coefficient of variation (CV)0.15882258
Kurtosis0.41651678
Mean77.969121
Median Absolute Deviation (MAD)8
Skewness-0.60431623
Sum65650
Variance153.34506
MonotonicityNot monotonic
2025-09-02T10:57:11.675551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 32
 
3.2%
90 29
 
2.9%
84 29
 
2.9%
85 27
 
2.7%
72 27
 
2.7%
86 27
 
2.7%
73 27
 
2.7%
81 26
 
2.6%
77 26
 
2.6%
80 26
 
2.6%
Other values (53) 566
56.7%
(Missing) 157
 
15.7%
ValueCountFrequency (%)
28 1
 
0.1%
30 1
 
0.1%
33 1
 
0.1%
36 1
 
0.1%
40 1
 
0.1%
41 1
 
0.1%
44 1
 
0.1%
45 3
0.3%
46 1
 
0.1%
47 4
0.4%
ValueCountFrequency (%)
100 12
1.2%
99 4
 
0.4%
98 9
0.9%
97 12
1.2%
96 18
1.8%
95 11
1.1%
94 20
2.0%
93 14
1.4%
92 13
1.3%
91 19
1.9%
Distinct548
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Memory size70.7 KiB
2025-09-02T10:57:12.863877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length22
Mean length13.485485
Min length7

Characters and Unicode

Total characters13472
Distinct characters69
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique353 ?
Unique (%)35.3%

Sample

1st rowFrancis Ford Coppola
2nd rowChristopher Nolan
3rd rowFrancis Ford Coppola
4th rowSidney Lumet
5th rowPeter Jackson
ValueCountFrequency (%)
john 34
 
1.6%
david 28
 
1.4%
james 23
 
1.1%
robert 20
 
1.0%
martin 16
 
0.8%
richard 15
 
0.7%
lee 15
 
0.7%
george 14
 
0.7%
steven 14
 
0.7%
alfred 14
 
0.7%
Other values (882) 1879
90.7%
2025-09-02T10:57:16.220082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1209
 
9.0%
a 1126
 
8.4%
1073
 
8.0%
n 950
 
7.1%
r 917
 
6.8%
o 851
 
6.3%
i 834
 
6.2%
l 543
 
4.0%
s 497
 
3.7%
t 433
 
3.2%
Other values (59) 5039
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10223
75.9%
Uppercase Letter 2107
 
15.6%
Space Separator 1073
 
8.0%
Other Punctuation 43
 
0.3%
Dash Punctuation 26
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1209
11.8%
a 1126
11.0%
n 950
 
9.3%
r 917
 
9.0%
o 851
 
8.3%
i 834
 
8.2%
l 543
 
5.3%
s 497
 
4.9%
t 433
 
4.2%
h 404
 
4.0%
Other values (26) 2459
24.1%
Uppercase Letter
ValueCountFrequency (%)
S 179
 
8.5%
A 171
 
8.1%
M 166
 
7.9%
J 162
 
7.7%
C 142
 
6.7%
R 131
 
6.2%
H 110
 
5.2%
B 106
 
5.0%
T 102
 
4.8%
D 99
 
4.7%
Other values (19) 739
35.1%
Other Punctuation
ValueCountFrequency (%)
. 41
95.3%
' 2
 
4.7%
Space Separator
ValueCountFrequency (%)
1073
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12330
91.5%
Common 1142
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1209
 
9.8%
a 1126
 
9.1%
n 950
 
7.7%
r 917
 
7.4%
o 851
 
6.9%
i 834
 
6.8%
l 543
 
4.4%
s 497
 
4.0%
t 433
 
3.5%
h 404
 
3.3%
Other values (55) 4566
37.0%
Common
ValueCountFrequency (%)
1073
94.0%
. 41
 
3.6%
- 26
 
2.3%
' 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13421
99.6%
None 51
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1209
 
9.0%
a 1126
 
8.4%
1073
 
8.0%
n 950
 
7.1%
r 917
 
6.8%
o 851
 
6.3%
i 834
 
6.2%
l 543
 
4.0%
s 497
 
3.7%
t 433
 
3.2%
Other values (46) 4988
37.2%
None
ValueCountFrequency (%)
ó 10
19.6%
á 9
17.6%
é 8
15.7%
ñ 7
13.7%
ô 5
9.8%
ö 3
 
5.9%
ç 2
 
3.9%
Ö 2
 
3.9%
Ô 1
 
2.0%
Ç 1
 
2.0%
Other values (3) 3
 
5.9%

Star1
Text

Distinct659
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Memory size70.3 KiB
2025-09-02T10:57:17.007774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length13.005005
Min length4

Characters and Unicode

Total characters12992
Distinct characters72
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique502 ?
Unique (%)50.3%

Sample

1st rowMarlon Brando
2nd rowChristian Bale
3rd rowAl Pacino
4th rowHenry Fonda
5th rowElijah Wood
ValueCountFrequency (%)
tom 22
 
1.1%
daniel 17
 
0.8%
robert 17
 
0.8%
john 16
 
0.8%
khan 16
 
0.8%
james 15
 
0.7%
michael 12
 
0.6%
hanks 12
 
0.6%
ethan 11
 
0.5%
de 11
 
0.5%
Other values (1112) 1898
92.7%
2025-09-02T10:57:18.492449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1239
 
9.5%
e 1088
 
8.4%
1048
 
8.1%
n 951
 
7.3%
r 816
 
6.3%
i 794
 
6.1%
o 767
 
5.9%
l 590
 
4.5%
t 453
 
3.5%
s 438
 
3.4%
Other values (62) 4808
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9784
75.3%
Uppercase Letter 2099
 
16.2%
Space Separator 1048
 
8.1%
Dash Punctuation 32
 
0.2%
Other Punctuation 29
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1239
12.7%
e 1088
11.1%
n 951
9.7%
r 816
 
8.3%
i 794
 
8.1%
o 767
 
7.8%
l 590
 
6.0%
t 453
 
4.6%
s 438
 
4.5%
h 424
 
4.3%
Other values (29) 2224
22.7%
Uppercase Letter
ValueCountFrequency (%)
C 187
 
8.9%
M 172
 
8.2%
J 144
 
6.9%
D 142
 
6.8%
B 142
 
6.8%
S 141
 
6.7%
R 126
 
6.0%
A 115
 
5.5%
H 106
 
5.1%
T 104
 
5.0%
Other values (19) 720
34.3%
Other Punctuation
ValueCountFrequency (%)
. 19
65.5%
' 10
34.5%
Space Separator
ValueCountFrequency (%)
1048
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11883
91.5%
Common 1109
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1239
 
10.4%
e 1088
 
9.2%
n 951
 
8.0%
r 816
 
6.9%
i 794
 
6.7%
o 767
 
6.5%
l 590
 
5.0%
t 453
 
3.8%
s 438
 
3.7%
h 424
 
3.6%
Other values (58) 4323
36.4%
Common
ValueCountFrequency (%)
1048
94.5%
- 32
 
2.9%
. 19
 
1.7%
' 10
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12937
99.6%
None 55
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1239
 
9.6%
e 1088
 
8.4%
1048
 
8.1%
n 951
 
7.4%
r 816
 
6.3%
i 794
 
6.1%
o 767
 
5.9%
l 590
 
4.6%
t 453
 
3.5%
s 438
 
3.4%
Other values (45) 4753
36.7%
None
ValueCountFrequency (%)
ô 13
23.6%
é 7
12.7%
ü 6
10.9%
í 6
10.9%
û 4
 
7.3%
ö 4
 
7.3%
è 3
 
5.5%
å 2
 
3.6%
ë 2
 
3.6%
Ç 1
 
1.8%
Other values (7) 7
12.7%

Star2
Text

Distinct659
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Memory size70.3 KiB
2025-09-02T10:57:19.636275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length13.005005
Min length4

Characters and Unicode

Total characters12992
Distinct characters72
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique502 ?
Unique (%)50.3%

Sample

1st rowMarlon Brando
2nd rowChristian Bale
3rd rowAl Pacino
4th rowHenry Fonda
5th rowElijah Wood
ValueCountFrequency (%)
tom 22
 
1.1%
daniel 17
 
0.8%
robert 17
 
0.8%
john 16
 
0.8%
khan 16
 
0.8%
james 15
 
0.7%
michael 12
 
0.6%
hanks 12
 
0.6%
ethan 11
 
0.5%
de 11
 
0.5%
Other values (1112) 1898
92.7%
2025-09-02T10:57:21.156807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1239
 
9.5%
e 1088
 
8.4%
1048
 
8.1%
n 951
 
7.3%
r 816
 
6.3%
i 794
 
6.1%
o 767
 
5.9%
l 590
 
4.5%
t 453
 
3.5%
s 438
 
3.4%
Other values (62) 4808
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9784
75.3%
Uppercase Letter 2099
 
16.2%
Space Separator 1048
 
8.1%
Dash Punctuation 32
 
0.2%
Other Punctuation 29
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1239
12.7%
e 1088
11.1%
n 951
9.7%
r 816
 
8.3%
i 794
 
8.1%
o 767
 
7.8%
l 590
 
6.0%
t 453
 
4.6%
s 438
 
4.5%
h 424
 
4.3%
Other values (29) 2224
22.7%
Uppercase Letter
ValueCountFrequency (%)
C 187
 
8.9%
M 172
 
8.2%
J 144
 
6.9%
D 142
 
6.8%
B 142
 
6.8%
S 141
 
6.7%
R 126
 
6.0%
A 115
 
5.5%
H 106
 
5.1%
T 104
 
5.0%
Other values (19) 720
34.3%
Other Punctuation
ValueCountFrequency (%)
. 19
65.5%
' 10
34.5%
Space Separator
ValueCountFrequency (%)
1048
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11883
91.5%
Common 1109
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1239
 
10.4%
e 1088
 
9.2%
n 951
 
8.0%
r 816
 
6.9%
i 794
 
6.7%
o 767
 
6.5%
l 590
 
5.0%
t 453
 
3.8%
s 438
 
3.7%
h 424
 
3.6%
Other values (58) 4323
36.4%
Common
ValueCountFrequency (%)
1048
94.5%
- 32
 
2.9%
. 19
 
1.7%
' 10
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12937
99.6%
None 55
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1239
 
9.6%
e 1088
 
8.4%
1048
 
8.1%
n 951
 
7.4%
r 816
 
6.3%
i 794
 
6.1%
o 767
 
5.9%
l 590
 
4.6%
t 453
 
3.5%
s 438
 
3.4%
Other values (45) 4753
36.7%
None
ValueCountFrequency (%)
ô 13
23.6%
é 7
12.7%
ü 6
10.9%
í 6
10.9%
û 4
 
7.3%
ö 4
 
7.3%
è 3
 
5.5%
å 2
 
3.6%
ë 2
 
3.6%
Ç 1
 
1.8%
Other values (7) 7
12.7%

Star3
Text

Distinct659
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Memory size70.3 KiB
2025-09-02T10:57:22.243434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length13.005005
Min length4

Characters and Unicode

Total characters12992
Distinct characters72
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique502 ?
Unique (%)50.3%

Sample

1st rowMarlon Brando
2nd rowChristian Bale
3rd rowAl Pacino
4th rowHenry Fonda
5th rowElijah Wood
ValueCountFrequency (%)
tom 22
 
1.1%
daniel 17
 
0.8%
robert 17
 
0.8%
john 16
 
0.8%
khan 16
 
0.8%
james 15
 
0.7%
michael 12
 
0.6%
hanks 12
 
0.6%
ethan 11
 
0.5%
de 11
 
0.5%
Other values (1112) 1898
92.7%
2025-09-02T10:57:23.773566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1239
 
9.5%
e 1088
 
8.4%
1048
 
8.1%
n 951
 
7.3%
r 816
 
6.3%
i 794
 
6.1%
o 767
 
5.9%
l 590
 
4.5%
t 453
 
3.5%
s 438
 
3.4%
Other values (62) 4808
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9784
75.3%
Uppercase Letter 2099
 
16.2%
Space Separator 1048
 
8.1%
Dash Punctuation 32
 
0.2%
Other Punctuation 29
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1239
12.7%
e 1088
11.1%
n 951
9.7%
r 816
 
8.3%
i 794
 
8.1%
o 767
 
7.8%
l 590
 
6.0%
t 453
 
4.6%
s 438
 
4.5%
h 424
 
4.3%
Other values (29) 2224
22.7%
Uppercase Letter
ValueCountFrequency (%)
C 187
 
8.9%
M 172
 
8.2%
J 144
 
6.9%
D 142
 
6.8%
B 142
 
6.8%
S 141
 
6.7%
R 126
 
6.0%
A 115
 
5.5%
H 106
 
5.1%
T 104
 
5.0%
Other values (19) 720
34.3%
Other Punctuation
ValueCountFrequency (%)
. 19
65.5%
' 10
34.5%
Space Separator
ValueCountFrequency (%)
1048
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11883
91.5%
Common 1109
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1239
 
10.4%
e 1088
 
9.2%
n 951
 
8.0%
r 816
 
6.9%
i 794
 
6.7%
o 767
 
6.5%
l 590
 
5.0%
t 453
 
3.8%
s 438
 
3.7%
h 424
 
3.6%
Other values (58) 4323
36.4%
Common
ValueCountFrequency (%)
1048
94.5%
- 32
 
2.9%
. 19
 
1.7%
' 10
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12937
99.6%
None 55
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1239
 
9.6%
e 1088
 
8.4%
1048
 
8.1%
n 951
 
7.4%
r 816
 
6.3%
i 794
 
6.1%
o 767
 
5.9%
l 590
 
4.6%
t 453
 
3.5%
s 438
 
3.4%
Other values (45) 4753
36.7%
None
ValueCountFrequency (%)
ô 13
23.6%
é 7
12.7%
ü 6
10.9%
í 6
10.9%
û 4
 
7.3%
ö 4
 
7.3%
è 3
 
5.5%
å 2
 
3.6%
ë 2
 
3.6%
Ç 1
 
1.8%
Other values (7) 7
12.7%

Star4
Text

Distinct659
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Memory size70.3 KiB
2025-09-02T10:57:24.827837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length13.005005
Min length4

Characters and Unicode

Total characters12992
Distinct characters72
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique502 ?
Unique (%)50.3%

Sample

1st rowMarlon Brando
2nd rowChristian Bale
3rd rowAl Pacino
4th rowHenry Fonda
5th rowElijah Wood
ValueCountFrequency (%)
tom 22
 
1.1%
daniel 17
 
0.8%
robert 17
 
0.8%
john 16
 
0.8%
khan 16
 
0.8%
james 15
 
0.7%
michael 12
 
0.6%
hanks 12
 
0.6%
ethan 11
 
0.5%
de 11
 
0.5%
Other values (1112) 1898
92.7%
2025-09-02T10:57:26.363897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1239
 
9.5%
e 1088
 
8.4%
1048
 
8.1%
n 951
 
7.3%
r 816
 
6.3%
i 794
 
6.1%
o 767
 
5.9%
l 590
 
4.5%
t 453
 
3.5%
s 438
 
3.4%
Other values (62) 4808
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9784
75.3%
Uppercase Letter 2099
 
16.2%
Space Separator 1048
 
8.1%
Dash Punctuation 32
 
0.2%
Other Punctuation 29
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1239
12.7%
e 1088
11.1%
n 951
9.7%
r 816
 
8.3%
i 794
 
8.1%
o 767
 
7.8%
l 590
 
6.0%
t 453
 
4.6%
s 438
 
4.5%
h 424
 
4.3%
Other values (29) 2224
22.7%
Uppercase Letter
ValueCountFrequency (%)
C 187
 
8.9%
M 172
 
8.2%
J 144
 
6.9%
D 142
 
6.8%
B 142
 
6.8%
S 141
 
6.7%
R 126
 
6.0%
A 115
 
5.5%
H 106
 
5.1%
T 104
 
5.0%
Other values (19) 720
34.3%
Other Punctuation
ValueCountFrequency (%)
. 19
65.5%
' 10
34.5%
Space Separator
ValueCountFrequency (%)
1048
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11883
91.5%
Common 1109
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1239
 
10.4%
e 1088
 
9.2%
n 951
 
8.0%
r 816
 
6.9%
i 794
 
6.7%
o 767
 
6.5%
l 590
 
5.0%
t 453
 
3.8%
s 438
 
3.7%
h 424
 
3.6%
Other values (58) 4323
36.4%
Common
ValueCountFrequency (%)
1048
94.5%
- 32
 
2.9%
. 19
 
1.7%
' 10
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12937
99.6%
None 55
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1239
 
9.6%
e 1088
 
8.4%
1048
 
8.1%
n 951
 
7.4%
r 816
 
6.3%
i 794
 
6.1%
o 767
 
5.9%
l 590
 
4.6%
t 453
 
3.5%
s 438
 
3.4%
Other values (45) 4753
36.7%
None
ValueCountFrequency (%)
ô 13
23.6%
é 7
12.7%
ü 6
10.9%
í 6
10.9%
û 4
 
7.3%
ö 4
 
7.3%
è 3
 
5.5%
å 2
 
3.6%
ë 2
 
3.6%
Ç 1
 
1.8%
Other values (7) 7
12.7%

Votes
Real number (ℝ)

High correlation 

Distinct998
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271621.42
Minimum25088
Maximum2303232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:57:26.771203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25088
5-th percentile29680
Q155471.5
median138356
Q3373167.5
95-th percentile939289.9
Maximum2303232
Range2278144
Interquartile range (IQR)317696

Descriptive statistics

Standard deviation320912.62
Coefficient of variation (CV)1.1814702
Kurtosis6.041324
Mean271621.42
Median Absolute Deviation (MAD)98475
Skewness2.1943511
Sum2.713498 × 108
Variance1.0298491 × 1011
MonotonicityNot monotonic
2025-09-02T10:57:27.259672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65341 2
 
0.2%
171640 1
 
0.1%
699256 1
 
0.1%
32802 1
 
0.1%
93878 1
 
0.1%
1213505 1
 
0.1%
51853 1
 
0.1%
1642758 1
 
0.1%
2067042 1
 
0.1%
1854740 1
 
0.1%
Other values (988) 988
98.9%
ValueCountFrequency (%)
25088 1
0.1%
25198 1
0.1%
25229 1
0.1%
25312 1
0.1%
25344 1
0.1%
25938 1
0.1%
26337 1
0.1%
26402 1
0.1%
26429 1
0.1%
26457 1
0.1%
ValueCountFrequency (%)
2303232 1
0.1%
2067042 1
0.1%
1854740 1
0.1%
1826188 1
0.1%
1809221 1
0.1%
1676426 1
0.1%
1661481 1
0.1%
1642758 1
0.1%
1620367 1
0.1%
1516346 1
0.1%

Revenue
Real number (ℝ)

High correlation  Missing 

Distinct822
Distinct (%)99.0%
Missing169
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean68082574
Minimum1305
Maximum9.3666222 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:57:27.723354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1305
5-th percentile139783.9
Q13245338.5
median23457440
Q380876340
95-th percentile2.9163069 × 108
Maximum9.3666222 × 108
Range9.3666092 × 108
Interquartile range (IQR)77631002

Descriptive statistics

Standard deviation1.0980755 × 108
Coefficient of variation (CV)1.6128584
Kurtosis13.894054
Mean68082574
Median Absolute Deviation (MAD)22698854
Skewness3.1277452
Sum5.6508537 × 1010
Variance1.2057699 × 1016
MonotonicityNot monotonic
2025-09-02T10:57:28.340369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4360000 5
 
0.5%
5321508 2
 
0.2%
5450000 2
 
0.2%
9600000 2
 
0.2%
25000000 2
 
0.2%
216540909 1
 
0.1%
49530280 1
 
0.1%
78756177 1
 
0.1%
292576195 1
 
0.1%
30500000 1
 
0.1%
Other values (812) 812
81.3%
(Missing) 169
 
16.9%
ValueCountFrequency (%)
1305 1
0.1%
3296 1
0.1%
3600 1
0.1%
6013 1
0.1%
6460 1
0.1%
7461 1
0.1%
8060 1
0.1%
10177 1
0.1%
10950 1
0.1%
12562 1
0.1%
ValueCountFrequency (%)
936662225 1
0.1%
858373000 1
0.1%
760507625 1
0.1%
678815482 1
0.1%
659325379 1
0.1%
623279547 1
0.1%
608581744 1
0.1%
534858444 1
0.1%
532177324 1
0.1%
448139099 1
0.1%

tmdb_budget
Real number (ℝ)

High correlation  Zeros 

Distinct284
Distinct (%)28.6%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean25839015
Minimum0
Maximum3.56 × 108
Zeros150
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:57:28.951046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11255000
median6900000
Q325000000
95-th percentile1.5 × 108
Maximum3.56 × 108
Range3.56 × 108
Interquartile range (IQR)23745000

Descriptive statistics

Standard deviation47120610
Coefficient of variation (CV)1.8236225
Kurtosis10.178
Mean25839015
Median Absolute Deviation (MAD)6900000
Skewness3.0205037
Sum2.5658142 × 1010
Variance2.2203519 × 1015
MonotonicityNot monotonic
2025-09-02T10:57:29.784283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 150
 
15.0%
15000000 30
 
3.0%
4000000 27
 
2.7%
25000000 21
 
2.1%
3000000 20
 
2.0%
12000000 19
 
1.9%
6000000 18
 
1.8%
2000000 18
 
1.8%
40000000 17
 
1.7%
30000000 17
 
1.7%
Other values (274) 656
65.7%
ValueCountFrequency (%)
0 150
15.0%
105 1
 
0.1%
3025 1
 
0.1%
18000 1
 
0.1%
27575 1
 
0.1%
64000 1
 
0.1%
114000 1
 
0.1%
120000 1
 
0.1%
133000 1
 
0.1%
150000 2
 
0.2%
ValueCountFrequency (%)
356000000 1
 
0.1%
300000000 1
 
0.1%
260000000 1
 
0.1%
250000000 7
0.7%
245000000 1
 
0.1%
237000000 1
 
0.1%
220000000 1
 
0.1%
200000000 6
0.6%
190000000 1
 
0.1%
185000000 1
 
0.1%

tmdb_popularity
Real number (ℝ)

High correlation 

Distinct983
Distinct (%)99.0%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean6.223619
Minimum0.0096
Maximum48.7572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:57:30.463733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0096
5-th percentile1.45766
Q12.6588
median4.3308
Q37.944
95-th percentile17.9107
Maximum48.7572
Range48.7476
Interquartile range (IQR)5.2852

Descriptive statistics

Standard deviation5.4444054
Coefficient of variation (CV)0.87479734
Kurtosis8.0700012
Mean6.223619
Median Absolute Deviation (MAD)2.1649
Skewness2.2926687
Sum6180.0537
Variance29.64155
MonotonicityNot monotonic
2025-09-02T10:57:31.161649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.2227 2
 
0.2%
1.6467 2
 
0.2%
6.6648 2
 
0.2%
1.6796 2
 
0.2%
2.581 2
 
0.2%
2.7535 2
 
0.2%
3.0026 2
 
0.2%
5.1589 2
 
0.2%
4.7199 2
 
0.2%
2.6488 2
 
0.2%
Other values (973) 973
97.4%
(Missing) 6
 
0.6%
ValueCountFrequency (%)
0.0096 1
0.1%
0.0336 1
0.1%
0.0486 1
0.1%
0.1994 1
0.1%
0.5302 1
0.1%
0.5579 1
0.1%
0.619 1
0.1%
0.7419 1
0.1%
0.7471 1
0.1%
0.7693 1
0.1%
ValueCountFrequency (%)
48.7572 1
0.1%
39.8771 1
0.1%
37.3574 1
0.1%
32.4134 1
0.1%
32.1482 1
0.1%
27.8211 1
0.1%
26.7541 1
0.1%
24.7742 1
0.1%
24.7217 1
0.1%
24.6837 1
0.1%

tmdb_vote_count
Real number (ℝ)

High correlation 

Distinct929
Distinct (%)93.6%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5496.4179
Minimum0
Maximum37863
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:57:31.774761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile339.2
Q11033
median2582
Q37816
95-th percentile19384.2
Maximum37863
Range37863
Interquartile range (IQR)6783

Descriptive statistics

Standard deviation6537.6424
Coefficient of variation (CV)1.1894369
Kurtosis3.4133625
Mean5496.4179
Median Absolute Deviation (MAD)1897
Skewness1.8374153
Sum5457943
Variance42740769
MonotonicityNot monotonic
2025-09-02T10:57:32.461058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
0.3%
286 3
 
0.3%
815 3
 
0.3%
4450 3
 
0.3%
1167 3
 
0.3%
1873 3
 
0.3%
334 2
 
0.2%
1981 2
 
0.2%
2774 2
 
0.2%
1499 2
 
0.2%
Other values (919) 967
96.8%
(Missing) 6
 
0.6%
ValueCountFrequency (%)
0 3
0.3%
1 1
 
0.1%
4 1
 
0.1%
11 1
 
0.1%
33 1
 
0.1%
100 1
 
0.1%
106 1
 
0.1%
128 1
 
0.1%
133 1
 
0.1%
135 1
 
0.1%
ValueCountFrequency (%)
37863 1
0.1%
37745 1
0.1%
34305 1
0.1%
32846 1
0.1%
32552 1
0.1%
31864 1
0.1%
30897 1
0.1%
30680 1
0.1%
29017 1
0.1%
28819 1
0.1%

tmdb_revenue
Real number (ℝ)

High correlation  Zeros 

Distinct832
Distinct (%)83.8%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1.3668567 × 108
Minimum0
Maximum2.923706 × 109
Zeros112
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-09-02T10:57:32.980684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14396821
median31500000
Q31.249 × 108
95-th percentile6.7463509 × 108
Maximum2.923706 × 109
Range2.923706 × 109
Interquartile range (IQR)1.2050318 × 108

Descriptive statistics

Standard deviation2.7556795 × 108
Coefficient of variation (CV)2.0160705
Kurtosis29.232896
Mean1.3668567 × 108
Median Absolute Deviation (MAD)31481879
Skewness4.4408458
Sum1.3572887 × 1011
Variance7.5937692 × 1016
MonotonicityNot monotonic
2025-09-02T10:57:33.551108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
 
11.2%
10000000 5
 
0.5%
8000000 5
 
0.5%
11000000 4
 
0.4%
23300000 3
 
0.3%
12000000 3
 
0.3%
25000000 3
 
0.3%
4000000 3
 
0.3%
30000000 3
 
0.3%
4500000 3
 
0.3%
Other values (822) 849
85.0%
(Missing) 6
 
0.6%
ValueCountFrequency (%)
0 112
11.2%
3193 1
 
0.1%
8811 1
 
0.1%
12678 1
 
0.1%
13422 1
 
0.1%
18121 1
 
0.1%
24173 1
 
0.1%
24517 1
 
0.1%
27105 1
 
0.1%
35274 1
 
0.1%
ValueCountFrequency (%)
2923706026 1
0.1%
2799439100 1
0.1%
2264162353 1
0.1%
2068223624 1
0.1%
2052415039 1
0.1%
1518815515 1
0.1%
1341511219 1
0.1%
1243225667 1
0.1%
1155046416 1
0.1%
1118888979 1
0.1%

Interactions

2025-09-02T10:56:49.165361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:38.844416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:47.859002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:53.651012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:59.190601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:05.341196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:12.121549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:17.902020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:24.766012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:29.962728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:36.923747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:41.573318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:49.664358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:39.304776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:48.328951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:54.051040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:59.707506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:05.785514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:12.522878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:18.299906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:25.268770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:30.383817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:37.321012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:42.244697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:50.303142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:39.766520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:48.777840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:54.481838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:00.453238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:06.241770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:13.031801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:18.789290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:25.768448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:30.930472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:37.714699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:42.757755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:50.735117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:40.222230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:49.187685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:54.962045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:00.970836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:06.786682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:13.465768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:19.280697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:26.148790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:31.362525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:38.022691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:43.294463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:51.162459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:40.669968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:49.708694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:55.358021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:01.566352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:07.224440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:13.860980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:19.716159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:26.570651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:31.784880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:38.343360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:43.849623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:52.130185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:44.278294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:50.313872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:55.857522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:02.097349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:07.852791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:14.784853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:20.272730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:26.980651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:32.228379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:38.897200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:44.761634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:52.774164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:44.846138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:50.818957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:56.375253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:02.492151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:08.417573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:15.225326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:20.864657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:27.448735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:32.659451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:39.238205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:45.295524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:53.237188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:45.280240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:51.285845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:56.757182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:03.020645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:08.952928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:15.701382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:21.348030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:27.858771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:33.109248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:39.595292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:45.802568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:53.789864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:45.759549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:51.686061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:57.158216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:03.444592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:09.552300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:16.069500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:21.788526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:28.324559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:33.539909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:39.843264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:46.384976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:54.346333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:46.319374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:52.116526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:57.638482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:03.937395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:10.262278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:16.507138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:22.619699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:28.739219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:34.224419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:40.094362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:46.988679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:54.891957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:46.733535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:52.602767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:58.054268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:04.442872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:10.933585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:16.952084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:23.153662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:29.114077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:34.894617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:40.377048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:47.793498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:55.456633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:47.409226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:53.066512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:55:58.575633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:04.882101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:11.525391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:17.440718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:24.353822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:29.573888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:35.800145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:40.957562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-02T10:56:48.339673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-02T10:57:33.990707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CertificateRatingRevenueRuntimeUnnamed: 0Unnamed: 0.1VotesYearscoreAvgtmdb_budgettmdb_popularitytmdb_revenuetmdb_vote_count
Certificate1.0000.0000.0630.1410.0720.0720.0570.3020.0880.0060.0610.0490.091
Rating0.0001.000-0.0500.210-0.992-0.9920.212-0.1270.285-0.1050.152-0.0290.113
Revenue0.063-0.0501.0000.1780.0360.0360.7000.175-0.1000.7640.6810.8980.720
Runtime0.1410.2100.1781.000-0.233-0.2330.1570.194-0.0900.3090.1720.2590.066
Unnamed: 00.072-0.9920.036-0.2331.0001.000-0.2450.012-0.2590.061-0.177-0.012-0.150
Unnamed: 0.10.072-0.9920.036-0.2331.0001.000-0.2450.012-0.2590.061-0.177-0.012-0.150
Votes0.0570.2120.7000.157-0.245-0.2451.0000.255-0.0730.6750.7900.7390.925
Year0.302-0.1270.1750.1940.0120.0120.2551.000-0.2640.4160.2240.3760.298
scoreAvg0.0880.285-0.100-0.090-0.259-0.259-0.073-0.2641.000-0.259-0.108-0.183-0.106
tmdb_budget0.006-0.1050.7640.3090.0610.0610.6750.416-0.2591.0000.6560.8070.687
tmdb_popularity0.0610.1520.6810.172-0.177-0.1770.7900.224-0.1080.6561.0000.7090.847
tmdb_revenue0.049-0.0290.8980.259-0.012-0.0120.7390.376-0.1830.8070.7091.0000.754
tmdb_vote_count0.0910.1130.7200.066-0.150-0.1500.9250.298-0.1060.6870.8470.7541.000

Missing values

2025-09-02T10:56:56.210825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-02T10:56:56.959128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-02T10:56:57.848803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0.1Unnamed: 0TitleYearCertificateRuntimeGenreRatingOverviewscoreAvgDirectorStar1Star2Star3Star4VotesRevenuetmdb_budgettmdb_popularitytmdb_vote_counttmdb_revenue
001The Godfather1972.0A175.0Crime, Drama9.2An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.100.0Francis Ford CoppolaMarlon BrandoMarlon BrandoMarlon BrandoMarlon Brando1620367134966411.06000000.024.774221774.02.450664e+08
112The Dark Knight2008.0UA152.0Action, Crime, Drama9.0When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.84.0Christopher NolanChristian BaleChristian BaleChristian BaleChristian Bale2303232534858444.0185000000.027.821134305.01.004558e+09
223The Godfather: Part II1974.0A202.0Crime, Drama9.0The early life and career of Vito Corleone in 1920s New York City is portrayed, while his son, Michael, expands and tightens his grip on the family crime syndicate.90.0Francis Ford CoppolaAl PacinoAl PacinoAl PacinoAl Pacino112995257300000.013000000.016.709513147.01.026000e+08
33412 Angry Men1957.0U96.0Crime, Drama9.0A jury holdout attempts to prevent a miscarriage of justice by forcing his colleagues to reconsider the evidence.96.0Sidney LumetHenry FondaHenry FondaHenry FondaHenry Fonda6898454360000.0397751.013.43219358.04.360000e+06
445The Lord of the Rings: The Return of the King2003.0U201.0Action, Adventure, Drama8.9Gandalf and Aragorn lead the World of Men against Sauron's army to draw his gaze from Frodo and Sam as they approach Mount Doom with the One Ring.94.0Peter JacksonElijah WoodElijah WoodElijah WoodElijah Wood1642758377845905.094000000.020.734525403.01.118889e+09
556Pulp Fiction1994.0A154.0Crime, Drama8.9The lives of two mob hitmen, a boxer, a gangster and his wife, and a pair of diner bandits intertwine in four tales of violence and redemption.94.0Quentin TarantinoJohn TravoltaJohn TravoltaJohn TravoltaJohn Travolta1826188107928762.08000000.017.246029017.02.139288e+08
667Schindler's List1993.0A195.0Biography, Drama, History8.9In German-occupied Poland during World War II, industrialist Oskar Schindler gradually becomes concerned for his Jewish workforce after witnessing their persecution by the Nazis.94.0Steven SpielbergLiam NeesonLiam NeesonLiam NeesonLiam Neeson121350596898818.022000000.017.402516671.03.213656e+08
778Inception2010.0UA148.0Action, Adventure, Sci-Fi8.8A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into the mind of a C.E.O.74.0Christopher NolanLeonardo DiCaprioLeonardo DiCaprioLeonardo DiCaprioLeonardo DiCaprio2067042292576195.0160000000.026.754137863.08.390306e+08
889Fight Club1999.0A139.0Drama8.8An insomniac office worker and a devil-may-care soapmaker form an underground fight club that evolves into something much, much more.66.0David FincherBrad PittBrad PittBrad PittBrad Pitt185474037030102.063000000.024.545630680.01.008538e+08
9910The Lord of the Rings: The Fellowship of the Ring2001.0U178.0Action, Adventure, Drama8.8A meek Hobbit from the Shire and eight companions set out on a journey to destroy the powerful One Ring and save Middle-earth from the Dark Lord Sauron.92.0Peter JacksonElijah WoodElijah WoodElijah WoodElijah Wood1661481315544750.093000000.023.981026322.08.713684e+08
Unnamed: 0.1Unnamed: 0TitleYearCertificateRuntimeGenreRatingOverviewscoreAvgDirectorStar1Star2Star3Star4VotesRevenuetmdb_budgettmdb_popularitytmdb_vote_counttmdb_revenue
989989990Giù la testa1971.0PG157.0Drama, War, Western7.6A low-life bandit and an I.R.A. explosives expert rebel against the government and become heroes of the Mexican Revolution.77.0Sergio LeoneRod SteigerRod SteigerRod SteigerRod Steiger30144696690.00.02.64881125.00.0
990990991Kelly's Heroes1970.0GP144.0Adventure, Comedy, War7.6A group of U.S. soldiers sneaks across enemy lines to get their hands on a secret stash of Nazi treasure.50.0Brian G. HuttonClint EastwoodClint EastwoodClint EastwoodClint Eastwood453381378435.04000000.02.7786756.05200000.0
991991992The Jungle Book1967.0U78.0Animation, Adventure, Family7.6Bagheera the Panther and Baloo the Bear have a difficult time trying to convince a boy to leave the jungle for human civilization.65.0Wolfgang ReithermanPhil HarrisPhil HarrisPhil HarrisPhil Harris166409141843612.04000000.07.35466428.0378000000.0
992992993Blowup1966.0A111.0Drama, Mystery, Thriller7.6A fashion photographer unknowingly captures a death on film after following two lovers in a park.82.0Michelangelo AntonioniDavid HemmingsDavid HemmingsDavid HemmingsDavid Hemmings56513NaN1800000.01.75831300.00.0
993993994A Hard Day's Night1964.0U87.0Comedy, Music, Musical7.6Over two "typical" days in the life of The Beatles, the boys struggle to keep themselves and Sir Paul McCartney's mischievous grandfather in check while preparing for a live television performance.96.0Richard LesterJohn LennonJohn LennonJohn LennonJohn Lennon4035113780024.0560000.01.9403706.011000000.0
994994995Breakfast at Tiffany's1961.0A115.0Comedy, Drama, Romance7.6A young New York socialite becomes interested in a young man who has moved into her apartment building, but her past threatens to get in the way.76.0Blake EdwardsAudrey HepburnAudrey HepburnAudrey HepburnAudrey Hepburn166544NaN2500000.04.70214312.09500000.0
995995996Giant1956.0G201.0Drama, Western7.6Sprawling epic covering the life of a Texas cattle rancher and his family and associates.84.0George StevensElizabeth TaylorElizabeth TaylorElizabeth TaylorElizabeth Taylor34075NaN5400000.02.8508721.032855818.0
996996997From Here to Eternity1953.0Passed118.0Drama, Romance, War7.6In Hawaii in 1941, a private is cruelly punished for not boxing on his unit's team, while his captain's wife and second-in-command are falling in love.85.0Fred ZinnemannBurt LancasterBurt LancasterBurt LancasterBurt Lancaster4337430500000.01650000.03.0902669.030500000.0
997997998Lifeboat1944.0NaN97.0Drama, War7.6Several survivors of a torpedoed merchant ship in World War II find themselves in the same lifeboat with one of the crew members of the U-boat that sank their ship.78.0Alfred HitchcockTallulah BankheadTallulah BankheadTallulah BankheadTallulah Bankhead26471NaN1590000.01.7674455.01000000.0
998998999The 39 Steps1935.0NaN86.0Crime, Mystery, Thriller7.6A man in London tries to help a counter-espionage Agent. But when the Agent is killed, and the man stands accused, he must go on the run to save himself and stop a spy ring which is trying to steal top secret information.93.0Alfred HitchcockRobert DonatRobert DonatRobert DonatRobert Donat51853NaN0.05.3971995.00.0